Importance:Most people with radiologically isolated syndrome (RIS) have high proportions of white matter lesions (WMLs) demonstrating the central vein sign (ie, central vein sign-positive lesion [CVS+L]) and at least 1 paramagnetic rim lesion (PRL), representing perivenular lesion development and chronic active demyelination, respectively. Whether these imaging measures predict developing clinical multiple sclerosis (MS) in people with RIS is not yet known. Objective:To determine the prognostic value of various magnetic resonance imaging (MRI) measures, particularly PRLs and CVS+L, in predicting clinical MS in people with RIS. Design, Setting, and Participants:This was a multicenter prospective cohort study conducted from 2011 and 2024. Participants older than 18 years and fulfilling published RIS criteria were consecutively recruited from 3 large academic MS centers. Exposures:Participants underwent 3-T MRI including brain and spinal cord (SC) sequences and longitudinal clinical assessments. MRIs were evaluated for the total WML, PRL, and SC lesion (SCL) counts as well as the proportion of CVS+L. Main Outcomes and Measures:The primary outcome was the development of clinical symptoms of MS. Time-varying Cox regression assessed the association between PRLs and symptom onset. Elastic net regression identified key predictors, incorporating PRLs, age, sex, and SCL. Results:A total of 79 eligible people with RIS were included (36 [46%] in the discovery cohort [DC], 43 [54%] in the validation cohort [VC]). Of the initial 46 DC participants, 10 withdrew or were lost to follow-up, whereas all VC participants completed follow-up. In the DC (median [IQR] age, 40 [31-51] years; 25 female [70%]; median [IQR] follow-up, 6.4 [5.0-9.1] years), 9 of 36 people with RIS (25%) developed MS (median [IQR] time, 5.2 [5.0-6.8] years). In the VC (median [IQR] age, 43 [36-51] years; 23 female [53%]; median [IQR] follow-up, 4.4 [2.5-7.9] years), 9 of 43 people with RIS (21%) developed MS (median [IQR] time, 4.4 [2.5-4.7] years). Higher PRL count was associated with earlier symptom onset between 5 and 30 years after initial RIS diagnosis (hazard ratio [HR], 1.15; 95% CI, 1.05-1.26; P = .004) in the DC, replicated in the VC (HR, 1.51; 95% CI, 1.00-2.27; P = .04). In the DC, having 4 or more PRLs (odds ratio [OR], 14.64; 95% CI, 2.00-207.23; P = .02) and higher PRL count (OR, 1.15; 95% CI, 1.03-1.32; P = .02) predicted clinical MS. In the VC, having any PRL was significantly associated with developing clinical MS (OR, 20.90; 95% CI, 2.35-533.30; P = .02). Conclusions and Relevance:Study findings suggest that accrual of nonresolving chronic inflammation in WML portends development of clinical MS in people with RIS, which may have clinical utility in guiding treatment decisions across the MS spectrum and strengthens the case for including asymptomatic MS in the diagnostic criteria. There is growing recognition that early detection of most chronic neurological diseases is critical to prevent or diminish future disability, and these findings are a specific example of how this principle might operate in practice.

The 2024 revisions of the McDonald diagnostic criteria for multiple sclerosis (MS) have incorporated susceptibility-based magnetic resonance imaging (MRI) biomarkers to improve diagnostic sensitivity and specificity. However, the addition of imaging sequences used to visualize these biomarkers (Time of Acquisition, TA: ~6 minutes) increases the overall patient scan time. This study addresses this by combining parallel imaging (TA: ~2 minutes) with our proposed deep learning-based image denoising method, complex-valued denoising convolutional neural network (ℂDnCNN), to generate high quality data. Network layers used in the denoising convolutional neural network (DnCNN) were extended to the complex domain for learning complex-valued MR image features in the image domain. Four real-valued and complex-valued versions of this network (2D DnCNN, 2D ℂDnCNN, 3D DnCNN, and 3D ℂDnCNN) were developed for testing on a simulated noise testing set and a real-world noise testing set. In the simulated noise testing set, 3D ℂ DnCNN outperformed the other approaches for denoising magnitude and complex MRI data across all levels of simulated noise. In the real-world noise testing set, 2D DnCNN yielded the highest increases for NRMSE, PSNR and CNR measures, while 3D DnCNN yielded the highest improvement for SSIM when denoising T2*-weighted magnitude images at the highest acceleration factor. For the complex-valued data, 3D ℂDnCNN outperformed 2D ℂDnCNN to produce high quality magnitude and phase data at all acceleration factors in all image quality metrics except for CNR measures. Overall, our study demonstrates the capability of deep learning-based image denoising methods to efficiently denoise ultra-fast submillimeter isotropic data. Our proposed ℂDnCNN is able to denoise complex-valued MRI data which further enables the generation of high-quality quantitative susceptibility mapping (QSM). Besides that, our experimental results using different denoising models indicate that CNNs should be designed to learn 3-dimensional image features in the complex domain to achieve optimal performance on MRI data.

BACKGROUND:The central vein sign (CVS) is a neuroimaging biomarker in multiple sclerosis (MS) with high diagnostic specificity. CVS is best detected with high-quality susceptibility-sensitive MRI sequences. For concurrent detection of lesions and veins, FLAIR* was developed as a post-processing method to provide contrast for T2 hyperintense lesions (FLAIR) and paramagnetic hypointense veins (T2*-weighted). Occasionally, CVS-like features have been noted on FLAIR, but the reliability of this finding is unknown. OBJECTIVE:To compare the central FLAIR hypointensity to FLAIR* CVS. METHODS:Scans from the CentrAl Vein Sign in MS (CAVS-MS) pilot study were included for the analysis. A blinded rater assessed all lesions for CVS on 3-tesla post-contrast FLAIR*. A second blinded rater assessed the same lesions for central hypointensity on FLAIR images alone. Counts were compared between methods. The same approach was applied for a subset with available non-contrast FLAIR* lesion ratings. RESULTS:With post-contrast FLAIR* CVS as the standard (n= 92; 1737 lesions), central FLAIR hypointensity demonstrated concordance of 64%, with sensitivity of 34% (95% CI, 30-37%) and specificity of 83% (95% CI, 81-85%). With non-contrast FLAIR* CVS as the standard (n= 38; 768 lesions), FLAIR demonstrated sensitivity of 40% (95% CI, 33-47%) and specificity of 85% (95% CI, 82-88%). Select 6 (≥6 central hypointense lesions) FLAIR was 59% accurate for a diagnosis of MS, with a lower specificity (63% vs. 90%, p= 0.008) in comparison to post-contrast FLAIR*. CONCLUSIONS:Assessment of CVS on FLAIR alone is unreliable and requires susceptibility-sensitive sequences to be clinically useful.

Background and ObjectivesThe 2024 McDonald criteria allow diagnosis of multiple sclerosis (MS) in individuals presenting with symptoms not specific for MS or incidental imaging findings suggestive of demyelination when supported by biomarker evidence, reflecting a shift toward diagnostic definitions increasingly grounded in biological mechanisms of disease. The diagnostic yield of these criteria in such populations has not been evaluated in multicenter cohorts. We aimed to determine the proportion of individuals with nonspecific or incidental imaging presentations who meet the 2024 McDonald criteria and describe the contribution of central vein sign and CSF oligoclonal bands (OCBs) to diagnostic classification.MethodsThis cross-sectional post hoc analysis used data from the Central Vein Sign in Multiple Sclerosis study, a multicenter observational cohort. Adults aged 18-65 years referred for diagnostic evaluation of possible MS were adjudicated by an expert panel. This analysis focused on participants with symptoms not specific for MS or incidental imaging findings suggestive of demyelination. Dissemination in space (DIS) and dissemination in time (DIT) were assessed using 2017 MRI criteria. Fulfillment of the 2024 McDonald criteria at baseline-the primary outcome-was determined using the Select-6 CVS and CSF OCBs. Select-6 assessment was available for all participants, whereas OCB data were available for a subset based on prior clinical evaluation.ResultsOf 420 participants enrolled, 191 (45%) presented with either nonspecific symptoms (n = 166) or incidental imaging findings (n = 25). The mean age was 42 years, and 78% were female. Thirty-six (19%) met the 2024 McDonald criteria at baseline, including 28 (17%) in the nonspecific symptom cohort and 8 (32%) in the incidental imaging cohort. Among 51 participants meeting 2017 DIS, 22 (43%) were Select-6 positive, 17 (33%) had positive OCBs, and 4 (8%) met 2017 DIT. Nonspecific sensory symptoms, visual disturbances, and subacute cognitive decline were most associated with a diagnosis of MS.DiscussionApplication of the 2024 McDonald criteria identified nearly one-fifth of individuals without typical presentations as meeting diagnostic criteria for MS at baseline. Biomarker incorporation-particularly the CVS-accounted for a substantial proportion of diagnostic yield. Interpretation is limited by availability of CSF data and absence of longitudinal follow-up.

ABSTRACT Objectives Retrograde trans‐synaptic degeneration (rTSD) from posterior visual pathway lesions in multiple sclerosis (MS) is characterized by hemi‐macular ganglion cell‐inner plexiform layer (GCIPL) thinning and contralateral visual field loss. We investigated associations between rTSD, paramagnetic rim lesions (PRL), and longitudinal visual disability in people with MS (pwMS) using a novel optical coherence tomography (OCT) based biomarker of rTSD. Methods PwMS, non‐MS neurological disease controls, and healthy controls underwent OCT, multiparametric brain MRI, and clinical assessments. A quantitative rTSD index was developed to capture hemispheric GCIPL asymmetry, with absolute values reflecting rTSD severity. Generalized linear models were used to investigate MRI and clinical predictors of rTSD. Longitudinal changes in rTSD were evaluated using mixed‐effects linear regression models. Results A total of 170 pwMS and 49 controls were included. PwMS had higher rTSD than healthy ( p = 0.006) and non‐MS controls ( p = 0.009). African American race ( p = 0.02) and longer disease duration ( p = 0.005) were associated with higher baseline rTSD. PRL in the optic radiations (OR) was linked to a 4.5‐fold increase in rTSD ( p = 0.037; n = 37) and −1.18 dB reduction in hemifield sensitivity ( p = 0.018). Longitudinally, each unit/year progression in rTSD was associated with a −0.07 dB/year decline in hemifield sensitivity ( p = 0.005) and a 10.8‐fold increase in the odds of expanded disability status scale score progression ( p = 0.022). Higher body mass index was associated with faster rTSD progression longitudinally ( p = 0.02). Interpretation PRL in the OR, African American race, disease duration, and higher BMI are associated with rTSD in pwMS. Longitudinal increase in rTSD is associated with worsening visual loss and clinical disability progression in MS. Trial Registration ClinicalTrials.gov identifier: NCT05204459

ABSTRACT Background The brain–heart axis is central to vascular health, yet no imaging biomarkers capture integrated dysfunction across neural and coronary microvascular networks. Although coronary microvascular dysfunction links to cognitive decline, neural correlates connecting cerebral efficiency with coronary physiology remain unclear. Objectives To determine whether the Unified Structural and Functional Connectivity (USFC)—a multimodal magnetic resonance imaging (MRI) “traffic map” of brain network efficiency—predicts coronary endothelial function and cognition in women with ischemia and no obstructive coronary artery disease (INOCA). Methods Thirty‐three women with suspected INOCA from the Women's Ischemia Syndrome Evaluation (WISE) study (NCT03876223) underwent invasive coronary function testing, cardiac MRI, cognitive evaluation, and multimodal brain MRI. USFC, structural connectivity (SC), and functional connectivity (FC) were computed for predefined 10 backbone pathways. Support vector regression and logistic classification assessed predictive performance. Results USFC explained 16%–20% more variance in coronary endothelial function, myocardial perfusion reserve, and cognition than SC or FC alone (p < 0.05). Connectivity between the left caudate–superior medial orbital gyrus and right calcarine–inferior occipital gyrus emerged as robust predictors of crystallized cognition (r = –0.78, pFDR < 0.05) and coronary endothelial function (r = 0.70, pFDR < 0.05), respectively. USFC also best discriminated low versus high coronary blood flow (area under the ROC curve [AUC]: USFC 0.622 vs. SC 0.349 and FC 0.425; p < 0.05). Conclusions USFC identifies neuro–cardiac pathways linking cerebral efficiency with coronary endothelial function. These results introduce a sensitive biomarker of systemic vulnerability, highlighting occipital and frontostriatal pathways as shared substrates of dysfunction. USFC offers a mechanistic framework for detecting vascular risk across metabolically demanding tissues. Trial Registration ClinicalTrials.gov identifier: NCT03876223

PURPOSE:Recent updates to the diagnostic criteria of multiple sclerosis (MS) require whole-brain T2*-weighted (T2*w) imaging with submillimeter resolution to detect novel diagnostic biomarkers such as the central vein sign. However, to achieve the needed submillimeter spatial resolution, conventional T2*w 3D gradient-echo scans sequences are limited by prohibitively long scan times for clinical use. Here, we evaluated a different approach based on a segmented 3D echo planar imaging (3D-EPI) sequence, accelerated with 2D Controlled Aliasing in Parallel Imaging Results in Higher Acceleration (CAIPIRINHA) undersampling and denoised with a deep learning-based network. METHODS:Fifty-two research participants were imaged at 3T using the 3D-EPI sequence acquired at different CAIPIRINHA acceleration factors (R = 2, 3, and 4) and denoised using a dedicated denoising convolutional neural network (DnCNN). Quantitative assessment of the accelerated T2*w 3D-EPI scans, before and after denoising, was performed using peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and tissue contrasts. A neuroradiologist separately assessed image quality in a blinded manner using predetermined scoring criteria. RESULTS:T2*w 3D-EPI with CAIPIRINHA acceleration enabled fast submillimeter isotropic (0.65 mm) imaging of the entire brain with scan times ranging between 3 min 22 s (R = 2) down to 1 min 56 s (R = 4). Even for the fastest scan (R = 4), accelerated T2*w 3D-EPI images denoised with DnCNN exhibited superior PSNR (3 dB increase), SSIM (13% increase) and lesion-to-vein tissue contrast (9% increase) compared to the non-denoised images. CONCLUSIONS:The 3D-EPI sequence combined with CAIPIRINHA and deep learning denoising enables fast submillimeter whole-brain T2*w imaging at 3T.

Multiple sclerosis diagnostic criteria lack optimal specificity, leading to potential misdiagnosis. Advanced magnetic resonance imaging (MRI) biomarkers like the central vein sign, cortical lesions and paramagnetic rim lesions are highly specific to multiple sclerosis and could potentially improve diagnostic accuracy. In this study, we applied machine learning techniques to a retrospective, multicentric dataset of 322 multiple sclerosis/multiple sclerosis-mimic (204/118) and 84 prodromal multiple sclerosis/non-multiple sclerosis (43/41) adult patients, incorporating the central vein sign, cortical lesions and paramagnetic rim lesions. We compared (5 × 2 cross-validation combined F-test) the diagnostic performance of 71 machine learning models, each corresponding to a distinct combination of full-count or simplified biomarker inputs, against the baseline dissemination in space McDonald criteria. The aim was to evaluate the multiple sclerosis diagnostic power of combining these biomarkers in an MRI-only diagnostic framework. 51 of the 71 models significantly outperformed the dissemination in space criterion (P < 0.05), with balanced accuracy improvements up to 13.0% (confidence interval: [+10.5; +17.0]). The best overall model (random forest, using full-count assessments) achieved 95.7% (confidence interval: [93.2; 99.7]) balanced accuracy; the best simplified model (logistic regression, using only simplified assessments) reached 94.7% with no significant difference with the former (P = 0.29). Notably, 12/51 high-performing models used only simplified assessments. To further investigate the models' generalizability, external validation on two out-of-distribution test sets using bootstrapping (1000 resamples) confirmed these results and highlighted a more robust generalization for the best model using solely simplified biomarkers. On the first external test set (n = 37, Verona), the simplified model achieved 97.2% balanced accuracy, while the full-count model reached 93.3% (versus 83.3% for baseline). On the second test set (n = 84, prodromal cases), the simplified model achieved 92.6% (versus 60.1% for baseline) showing competitive performance against the full-count model (93.9%). Both models improved all key performance metrics-balanced accuracy, sensitivity, specificity, precision and F1 score-over the baseline on both test sets (all P < 0.0001). Within a non-invasive MRI-only diagnostic framework, these results show that the incorporation of advanced imaging biomarkers into the multiple sclerosis-MRI diagnostic criteria significantly enhances the diagnostic accuracy-a statement holding true even when using simplified central vein sign, cortical lesions and paramagnetic rim lesions assessments. The study also provides a publicly available online diagnostic tool, facilitating further interaction, validation and clinical support (https://www.msdiagnostictool.org).

BACKGROUND:The central vein sign (CVS) and the paramagnetic rim lesion (PRL) are neuroimaging biomarkers of multiple sclerosis (MS). OBJECTIVES:To determine the diagnostic performance of CVS and PRL integration in people presenting for initial diagnostic evaluation of MS. METHODS:Adults with clinical/radiological suspicion of MS, aged 18-65, with CVS and PRL assessment from the CentrAl Vein Sign in MS (CAVS-MS) pilot were included. Diagnostic performance of CVS and PRL combinations was evaluated, with 2017 McDonald criteria as the reference standard. RESULTS:Seventy-eight participants were included (71% female, 86% white, 37 with MS). The combination of ⩾1 CVS and ⩾1 PRL demonstrated sensitivity/specificity of 0.76 (95% CI, 0.59, 0.88) and 0.93 (95% CI, 0.80, 0.98). The combination of ⩾6 CVS and ⩾1 PRL demonstrated sensitivity/specificity of 0.57 (95% CI, 0.39, 0.73) and 1.00 (95% CI, 0.91, 1.00). In those with cerebrospinal fluid testing (n = 46, 24 with MS), ⩾6 CVS and ⩾1 PRL diagnostic specificity was 1.00 (95% CI, 0.85, 1.00), similar to that of oligoclonal bands and dissemination in space by magnetic resonance imaging (0.82 [95% CI, 0.60, 0.95]). DISCUSSION:Integrating CVS and PRL represents potentially advantageous MS diagnostic biomarkers, with increased specificity and without substantial reduction in sensitivity.

The 2024 revision of the McDonald diagnostic criteria is an important step toward earlier and more inclusive diagnosis of multiple sclerosis. Realizing its full potential will require continued refinement of biomarkers, disease stratification and clinical trial approaches.

BACKGROUND AND PURPOSE:Paramagnetic rim lesions (PRLs) are a highly specific imaging biomarker for MS that are now integrated into the 2024 McDonald criteria. PRLs can be detected on standard SWI, which combines data from the homodyne-filtered phase (phase) and enhanced magnitude (SWI) images, as well as on the phase images alone. However, the relative visibility of PRL on SWI vs phase images is unknown, and it remains unclear which of these contrasts should be used for PRL evaluation in the clinical setting. MATERIALS AND METHODS:The study sample (n = 40) consisted of 2 cross-sectional cohorts-a primary cohort including both MS and non-MS cases (n = 20) and a distinct secondary cohort consisting of MS participants only (n = 20)-both selected from a prospective, multicenter observational study (Central Vein Sign: A Diagnostic Biomarker in Multiple Sclerosis Study) of participants presenting for initial diagnostic evaluation of suspected MS. Standardized brain MRI sequences (3D T1WI with and without contrast, T2-FLAIR, and vendor-provided SWI) were acquired at 3T. T2-hyperintense lesions were evaluated on SWI and phase images for classification as PRLs or non-PRLs in separate trials by trained, blinded raters (2 raters in the primary cohort, 1 rater in the secondary cohort). Differences in PRL frequency between SWI and phase images were analyzed with the McNemar test in the primary, secondary, and pooled cohorts. RESULTS:One hundred fifty-five PRLs were identified in the pooled cohort, of which 66 (42.6%) were detected only on phase images. The number of lesions classified as PRL was 2.7- and 1.4-fold greater when evaluated on phase images compared with SWI in the primary (43 vs 16, P < .001) and secondary (109 vs 73, P < .001) cohorts, respectively, and 1.7-fold greater in the pooled cohort (152 vs 89, P < .001). The main reasons for non-PRL classification on SWI were nodular appearance (37.9%) and discontinuity of the rim (28.8%). CONCLUSIONS:Filtered phase images derived from standard SWI outperform enhanced magnitude images for PRL identification and should be primarily used for PRL evaluation in clinical practice.

Ischemia with no obstructive coronary artery disease (INOCA), often due to coronary microvascular dysfunction (CMD), disproportionately affects women and may be linked to cognitive impairment and increased risk of dementia. While CMD and cerebral small vessel disease (CSVD) share similar risk factors and may contribute to cognitive decline, the mechanistic pathways connecting these conditions in women remain unclear. We conducted a cross-sectional observational study with a planned enrollment of 100 women aged 18 years and older with symptoms of INOCA and suspected CMD, recruited from the National Heart, Lung, and Blood Institute-sponsored Women’s Ischemia Syndrome Evaluation–Pre-Heart Failure with Preserved Ejection Fraction study (ClinicalTrials.gov identifier: NCT03876223) and the Microvascular Aging and Eicosanoids–Women’s Evaluation of Systemic Aging Tenacity (MAE-WEST) (“You are never too old to become younger!”) Specialized Center for Research Excellence (SCORE) (U54AG065141) studies at Cedars–Sinai Medical Center and the University of Florida. Each participant underwent a comprehensive assessment protocol, including advanced brain magnetic resonance imaging to quantify markers of CSVD, cardiac MRI to evaluate CMD, non-mydriatic retinal imaging, peripheral vascular function testing, and an extensive battery of cognitive assessments. Clinical, sociodemographic, and vascular risk factor data were collected. We analyzed cross-sectional associations between multimodal imaging biomarkers and cognitive performance. This protocol describes the first multidimensional, imaging-based investigation to integrate assessments of CMD, CSVD, retinal microvasculature, and cognitive function in women at risk for INOCA. Findings will enhance our understanding of the shared vascular mechanisms underlying cognitive decline and inform strategies for early intervention in at-risk women.

BACKGROUND AND OBJECTIVES:Paramagnetic rim lesions (PRLs) are a well-established imaging biomarker of chronic active multiple sclerosis (MS) lesions. PRLs have been shown to be highly specific for MS (∼90% specificity), and their prevalence has been estimated to be approximately 50% in patients with clinically established diagnoses of MS. In this study, we evaluated the frequency and diagnostic value of PRLs in patients at first clinical presentation. METHODS:Adults age 18-64 years presenting with clinical symptoms or radiologic suspicion of demyelinating disease referred to academic specialty MS centers without a definitive diagnosis were prospectively enrolled in a multicenter, cross-sectional, observational study. Phase images from high-resolution 3D echo-planar imaging were acquired on 3-tesla brain MRI and evaluated for PRLs by 3 independent raters, blinded to diagnosis, with adjudication from a fourth expert rater. Diagnostic performance of PRLs for a diagnosis of MS using the 2017 McDonald criteria as gold standard was evaluated using diagnostic thresholds based on the presence of at least 1 PRL (≥1 PRL) or at least 2 PRLs (≥2 PRLs). RESULTS:Seventy-eight participants were analyzed (mean age, years [range]: 45.0 [18-64] female sex n = 55 [71%]); a total of 124 PRLs were counted in 36 (46%) of the 78 participants (median: 3 PRLs; range: 1-9 PRLs). Thirty-two (89%) of the 36 PRL-positive participants fulfilled 2017 McDonald criteria, and the remaining 4 (11%) had an alternate diagnosis. The presence of ≥1 PRL had a sensitivity of 0.86 (95% CI 0.71-0.95) and a specificity of 0.90 (95% CI 0.77-0.97). For ≥2 PRLs, specificity increased to 0.95 (95% CI 0.83-0.99), whereas sensitivity decreased to 0.59 (95% CI 0.42-0.75). For participants with MS, shorter duration from initial symptom onset was associated with higher probability of having PRLs, with the odds of being PRL positive (≥1 PRL) increasing by 28% for every 1 year decrease from symptom onset (odds ratio 1.28 per year, 95% CI 1.03-1.59, p = 0.03). DISCUSSION:PRLs are highly prevalent early in patients with MS at the time of first clinical presentation and can differentiate MS from mimics with high accuracy.

MRI plays an increasingly important role in the diagnosis of multiple sclerosis. We discuss the expanded role of MRI in the 2024 McDonald diagnostic criteria for multiple sclerosis, which include the optic nerve as a fifth anatomical location, in addition to the periventricular, juxtacortical or cortical, infratentorial, and spinal cord regions. The diagnosis of multiple sclerosis can now be confirmed when the criteria of dissemination in space are fulfilled with the detection of typical lesions in at least four locations without additional evidence. We recommend appropriate imaging strategies and MRI acquisition protocols for all aspects of multiple sclerosis diagnosis, including fat-saturated sequences for detection of symptomatic optic nerve lesions. Diagnostic imaging should always cover the brain and spinal cord and include susceptibility-sensitive sequences for the assessment of the central vein sign and paramagnetic rim lesions, which can be especially helpful in cases when conventional imaging findings are insufficient to establish a diagnosis. We discuss how to handle the diagnosis of radiologically isolated presentations of multiple sclerosis, which are included in the 2024 criteria. We present recommendations for image interpretation and avoidance of misdiagnosis, and extend the recommendations to the use of MRI in the diagnosis of multiple sclerosis in older people, children, people with vascular comorbidities or migraine, and people living outside Europe and North America. Finally, we provide recommendations for standardisation of MRI acquisition and communication of results to enable an earlier diagnosis while maintaining high diagnostic specificity.

ABSTRACTBackground and PurposeThe central vein sign (CVS) is a diagnostic imaging biomarker for multiple sclerosis (MS). FLAIR* is a combined MRI contrast that provides high conspicuity for CVS at 3 Tesla (3T), enabling its sensitive and accurate detection in clinical settings. This study evaluated whether CVS conspicuity of 3T FLAIR* is reliable across imaging sites and MRI vendors and whether gadolinium (Gd) contrast increases CVS conspicuity.MethodsA cross‐sectional, multicenter study recruited adults referred for possible diagnosis of MS at 10 sites. FLAIR* contrast was generated using high‐resolution T2*‐weighted (acquired pre‐ and post‐injection of Gd) and T2‐weighted fluid‐attenuated inversion recovery (T2‐FLAIR) brain images at 3T from two MRI vendors. Lesions and veins were segmented to compute lesion‐to‐vein contrast‐to‐noise ratio (CNRlesion‐to‐vein), a quantitative measure of CVS conspicuity. CNRlesion‐to‐vein measures for pre‐ and post‐Gd FLAIR* were compared across sites and vendors.ResultsEighty‐seven participants from nine sites were included in the analysis. There was no significant difference in mean CNRlesion‐to‐vein between sites for pre‐Gd (p‐value = 0.07) or post‐Gd (p‐value = 0.27) FLAIR*. There were also no significant differences between vendors for pre‐Gd (p‐value = 0.10) or post‐Gd (p‐value = 0.31) FLAIR*. Patient‐level pairwise differences in CNRlesion‐to‐vein between pre‐Gd and post‐Gd FLAIR* revealed a significant increase for post‐Gd FLAIR* (p‐value < 0.001).ConclusionsCVS conspicuity on 3T FLAIR* is consistent across imaging sites and MRI vendors. Moreover, Gd‐based contrast agent significantly improved CVS conspicuity on 3T FLAIR*. These findings support the implementation of FLAIR* in clinical settings for MS.

Multiple sclerosis (MS) is characterized by central nervous system lesions detectable via MRI. Existing diagnostic criteria incorporate presence of white matter lesions, but specificity can be improved using MS-specific imaging biomarkers, including paramagnetic rim lesions (PRLs) and central vein sign (CVS). However, manual segmentation of lesions, PRLs, and CVS is time-consuming and subjective. We propose a fully-automated joint segmentation method called Automated Lesion, PRL, and CVS Analysis (ALPaCA). We trained ALPaCA using subject-level cross-validation on 47 adults with MS and 50 adults with radiological MS mimics. ALPaCA uses a voxel-wise lesion segmentation method to propose a large set of lesion candidates. Lesion candidates are input into a multi-contrast, multi-label 3D convolutional neural network as 3D patches to produce lesion, PRL, and CVS predictions. When multiple lesions exist within a patch, an attention mechanism identifies which lesion candidate to classify. At the lesion level, ALPaCA achieves cross-validation areas under the receiver operating characteristic curve (AUROCs) of 0.95, 0.91, and 0.87 for lesion, PRL, and CVS classification, outperforming previous methods (all p < 0.001). Correlations between subject-level ALPaCA lesion and PRL scores with manual counts are higher than those of previous methods (p < 0.001; p = 0.03). Subject-level ALPaCA PRL and CVS scores are highly associated with MS in logistic regressions, when controlling for age and sex (p < 0.001). ALPaCA allows for fully-automated simultaneous segmentation of MS lesions, PRLs, and CVS using clinically-feasible scans. These segmentations outperform existing methods at the lesion and subject level.

Abstract Multiple sclerosis (MS) is an inflammatory demyelinating disease of the central nervous system (CNS) and is a leading non-traumatic cause of disability in young adults. The 18 kDa Translocator Protein (TSPO) is a mitochondrial protein and positron emission tomography (PET)-imaging target that is highly expressed in MS brain lesions. It is used as an inflammatory biomarker and has been proposed as a therapeutic target. However, its specific pathological significance in humans is not well understood. Experimental autoimmune encephalomyelitis (EAE) in the common marmoset is a well-established primate model of MS. Studying TSPO expression in this model will enhance our understanding of its expression in MS. This study therefore characterizes patterns of TSPO expression in fixed CNS tissues from one non-EAE control marmoset and 8 EAE marmosets using multiplex immunofluorescence. In control CNS tissue, we find that TSPO is expressed in the leptomeninges, ependyma, and over two-thirds of Iba1 + microglia, but not astrocytes or neurons. In Iba1 + cells in both control and acute EAE tissue, we find that TSPO is co-expressed with markers of antigen presentation (CD74), early activation (MRP14), phagocytosis (CD163) and anti-inflammatory phenotype (Arg1); a high level of TSPO expression is not restricted to a particular microglial phenotype. While TSPO is expressed in over 88% of activated Iba1 + cells in acute lesions in marmoset EAE, it also is sometimes observed in subsets of astrocytes and neurons. Additionally, we find the percentage of Iba1 + cells expressing TSPO declines significantly in lesions > 5 months old and may be as low as 13% in chronic lesions. However, we also find increased astrocytic TSPO expression in chronic-appearing lesions with astrogliosis. Finally, we find expression of TSPO in a subset of neurons, most frequently GLS2 + glutamatergic neurons. The shift in TSPO expression from Iba + microglia/macrophages to astrocytes over time is similar to patterns suggested by earlier neuropathology studies in MS. Thus, marmoset EAE appears to be a clinically relevant model for the study of TSPO in immune dysregulation in human disease. Graphical Abstract

Background Optic neuritis (ON) is a common manifestation of multiple sclerosis and related disorders (MSRD) characterized by retinal neurodegeneration, including thinning of ganglion cell-inner plexiform layer (GCIPL). Compared to absolute values, inter-eye differences (IED) account for variation in baseline structure and function before ON. Objectives To determine retinal layer IED thresholds associated with multimodal visual dysfunction after unilateral demyelinating ON. Methods In this cross-sectional study, MSRD participants with and without a history of unilateral ON, and healthy controls underwent optical coherence tomography, best-corrected visual acuity, 2.5% and 1.25% low-contrast letter acuity (LCLA), standard automated perimetry, and color vision testing. Results Sixty-six participants with MSRD (33 with unilateral ON history, 33 without ON history) and 15 healthy controls were included. For the ON cohort, a GCIPL IED threshold of 6.5% was associated with dyschromatopsia (AUC = 0.76, p = 0.011), 11% with 1.25% LCLA IED of >5 letters (AUC = 0.75, p = 0.008), 13% with 2.5% LCLA IED of >5 letters (AUC = 0.86, p < 0.001), 15% with VFMD IED of >2 dB (AUC = 0.75, p = 0.031), and 27% with logarithm of minimum angle of resolution IED of >0.3 (AUC = 1.00, p < 0.001). These associations were more robust compared to other retinal layer IED. Conclusions GCIPL IED thresholds more accurately reflect multimodal visual dysfunction after ON compared to other retinal layer IED.